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Demand interactions between multiple commodity and non-commodity outputs This is possibly the most important single valuation issue that is specific to multiple-effect policies: demand interactions may cause severe aggregation problems when summing up values over different policy effects; this aggregation problem does not depend on the way the multiple effects were generated (e.g. joint production), but simply on demand-side considerations. Demands for either commodity or non-commodity outputs derived in (4) and (5) depend not only on all prices but also on levels of all non-commodity outputs.

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So, we can define demand interaction as shifts in demand curves caused by changes in prices or non-commodity output levels. For example, we say that a commodity a and a non- commodity output b are substitutes (complements) if demand for a is reduced (increased) by an increase in the level of non-commodity output b, i.e. if: x a C (.) / z b = – 2 CV i (.)/ p a z b ) 0 (6) Along the same lines, we say that two non-commodity outputs a and b are substitutes (complements) when the marginal value of a is reduced (increased) by an increase in the level of b, i.e. if: a C (.) / z b = 2 CV i (.)/ z a z b ) 0 (7)

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Such an example of a downward shift of the marginal- value curve for a due to an increase in the level of b is illustrated in the following graph. Figure 1. downward shift of marginal-value for a due to an increase in a substitutes level

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Deriving the value of a multiple-output policy from demands for the different outputs Assume there is a policy aimed at increasing the levels of non-commodity outputs 1 and 2 (think e.g. of two landscape elements, or habitat resources) to (z 1, z 2 ) as compared to their policy-off levels (z 1 0, z 2 0 ). Assume that, for visitors to the targeted area, the change in these two non-commodity outputs is the only fact affecting their well-being (i.e.: they do not suffer income or price changes caused by the policy).

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From equation (5), we know that the demand for each non-commodity output is derived by differentiating compensating variation (CV), that is WTP for the overall change, with respect to the corresponding non- commodity output. Hence, getting WTP for the overall change will require simply integrating demands. How to compute a visitors WTP for the beneficial change in the two non-commodity outputs from his (her) demand functions for the two attributes?

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Sequential as opposed to independent values Which integration path should we adopt to integrate demands? Consider two types of such a path: a sequential path, with 2 steps, such as (z 1 0, z 2 0 ) (z 1, z 2 0 ) followed by (z 1, z 2 0 ) (z 1, z 2 ), where we change first output 1, with 2 at its initial level, and then output 2, with 1 at its final level; this is a sequential path; a non-sequential path where we value every single output change as if it was the next change to status quo, i.e. with the other outputs at their initial levels; this leads us to consider 2 changes: (z 1 0, z 2 0 ) (z 1, z 2 0 ), and (z 1 0, z 2 0 ) (z 1 0, z 2 ).

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The IVS bias The IVS result is, in general, different from the unbiased CV measure of WTP for the overall multiple-output change. The difference is known as the IVS bias: IVS bias = IVS i - CV i (10) or, in the per-cent form: IVS bias (%) = 100 x (IVS i / CV i – 1) (11) The following graph illustrates the CV and IVS results, as well as the IVS bias.

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Aggregating across policy effects and across individuals: two separate problems If SV in is the sequential value of policy effect n for individual i, the unbiased CV measure of individual is WTP for the multiple-effect policy is: (16) Using the cost-benefit rule to aggregate over individuals yields: (17)

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Equation (17) shows that, provided we use sequential values, we can either: (1) integrate individual demands for each output, aggregate over outputs and, eventually, summing up over individuals as in the LHS, or ( 2) integrate aggregate demands for each output, and then summing up over outputs.

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Thus demonstrating that demand interactions are not a concern for summing up values over individuals, but only for summing up values over policy effects – using IVs instead of SVs, equations (16) and (17) would not hold. This is intuitive, as demand interactions between policy effects are internal to each individual. Thus, the issues of aggregating across individuals and across policy effects are two separate problems.

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Practical importance of the IVS bias The importance of the IVS bias is very often underestimated in practice. It is one of the greater (if not the greatest) limitations to valuation and cost-benefit analysis of multiple-effect policy changes, because: most usual valuation methods, based on either original valuation studies or valuation information transferred from previous studies, are prone to this bias; the IVS bias may be sufficiently large to lead to the wrong policy recommendations in many practical cases – a fact which we refer to as the practical importance, as opposed to the statistical significance, of bias.

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Predicting the sign and size of the IVS bias With large numbers of non-commodity outputs, IVS will overstate the unbiased value of the overall multiple-output policy – theoretical proof given by Hoehn and Randall (1989); with smaller numbers of non-commodity outputs, the sign and size of the IVS bias will, in general, be undetermined, because there may be offsetting effects between substitution and complementarity effects; yet, even in these smaller-number cases, Hoehn (1991) and Santos (1998) suggested that demand substitution between non- commodity outputs will be more frequent than complementarity; thus IVS would very often lead to overvaluation of true WTP; evidence presented by Hoehn (1991) and Hoehn and Loomis (1993) matches this expectation.

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Empirical evidence in the context of multifunctional agriculture Study 1. Pennine Dales Environmentally Sensitive Area (ESA) study (Santos 1998); - carried out in 1995; - 422 usable questionnaires administered to visitors to the area; - each visitor asked about weather would pay a specified amount for a particular mix of three basic programmes; - P1 was stone walls and barns conservation; P2, the conservation of flower diversity in hay meadows; and P3, the conservation of small broad-leaved woods.

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(1) the continuance of a specified ESA scheme at a given tax-rise cost (2) giving up the scheme with no tax increase

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Hypothetical policy-mixes were built by combining three basic programmes: P1, or the conservation of existing stone walls and field barns P2, or the conservation of flower-rich hay meadows P3, or the conservation of remaining small broad-leaved woods

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- these were combined in different area proportions (e.g. 50%: 50%, or 50%: 25%); - thus we got WTP for any combination of these land uses, plus abandoned area in the remaining percentage of land (Madureira 2000); - here, we compare demand interactions for the two different populations and 2 different such land-use combinations.

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Discussion of results in table 1 First, for all the 13 multiple-output bundles, the IVS bias is positive (and statistically significant), implying substitution relationships prevail among the several non-commodity outputs in the bundle; estimates from the Pennine Dales ESA case suggest that the IVS bias increases with increase in the size of the bundle from 2 to 3 outputs; this trend is not confirmed by the Peneda-Gerês results, where respondents were prepared to pay a large premium to have the 3 most important landscape attributes conserved altogether; which leads to complementarity in demand when the third output is added to the bundle;

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thus, while, in general, the IVS bias will eventually rise as the number of outputs in the bundle grows large (Hoehn and Randall 1989), more empirical research is required to predict IVS trends with respect to increasing bundle size in cases of small numbers of policy outputs; the more similar two outputs are the more close substitutes we would expect they are; thus, purely aesthetic/cultural landscape attributes, such as stone- walls would be poor substitutes for meadows and woods, largely perceived by respondents as wildlife habitats, and not purely aesthetic elements; the results in table 1 strongly support this prediction;

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the fourth case-study allows us to check what happens to the size of the IVS bias (thus the strength of substitution) when the amount of one of the outputs is increased; for both visitors and the general public, the IVS bias is significantly increased with the rise from 25% to 50% in the share of afforested land; this study also enables us to compare the size of the IVS bias between two populations; substitution between almond tree orchards and afforested land is much stronger for visitors than among the general public; for the former, the value of the output-bundle actually declines with the amount of afforested land, despite the fact that afforested (as compared to abandoned) land is itself a good, not a bad.

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It is the IVS bias sufficiently large to lead to wrong policy decisions being recommended by the cost-benefit analysis? i.e. it is the bias practically important? (as opposed to statistically significant). For the Pennine Dales ESA case, the unbiased social benefit-cost ratios were very large; thus the (overestimated) IVS benefit-cost ratios could only lead to the same policy recommendation as unbiased ones, that is: going ahead with any of those output bundles; note, however, that this result heavily depends on the particular characteristics of this policy case, in particular the fact that true benefits were here much larger than costs;

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in the Peneda-Gerês case, the unbiased social benefit- cost ratios for the several output-bundles were in the range 0.65-1.43, with one case at exactly 1,00; policy recommendations to go ahead with scheme are much weaker here than in the previous policy case; however, IVS biases were not sufficiently large to invert the unbiased policy recommendations; yet in some cases they lead to perhaps too overconfident recommendations of going ahead with the policy; in many cases, especially in complex real-world policy cases, where the number of non-commodity outputs of policy is much larger than 2 or 3, the practical importance of IVS biases will be much greater.

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Recommendations to improve usual valuation practices to avoid practically important biases Using a valuation approach, such as that used in the 4 studies above, which, by jointly valuing the several multiple-output changes, automatically takes into account substitution effects; extending research, using this approach, to different policy contexts, to check whether the trends identified above are confirmed and some general pattern emerges (Randall 1991);

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if this is the case, this pattern could be modelled to predict the sign and magnitude of the IVS bias for the specific circumstances of any policy that we need values for; this would generate adjusting factors, which would eventually help to correct empirical benefit measures estimated by using current, IVS-bias-prone, approaches; this systematic generation of adjusting factors will probably come up as the most cost-effective solution, as most (cheaper) empirical approaches to valuation, especially benefit transfers, are based on piecewise (i.e. independent) valuation of effects followed by summing up across effects – i.e. IVS is generally used.

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Non-market valuation methods We briefly review here the potential of different valuation methods to value multiple non-commodity outputs of land in ways that take account of demand interactions between these outputs. Valuation methods can be classified according to two criteria: the type of data used, i.e.: hypothetical as opposed to behavioural data; the way used to ask things to people, or to reveal value from collected data, which can be direct or indirect.

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Hypothetical, direct methods This is the case of the Open-ended (OE) version of the Contingent Valuation Method (CVM), which directly asks people exactly what we need to know, that is WTP: (1) how much would you be prepared to pay as an entrance fee to this particular recreation site? Hypothetical, indirect methods In this case, we ask people to make choices that are more indirectly related to WTP. To reveal WTP values from peoples answers some type of modelling is required. Some examples are given here.

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One is the discrete-choice (DC) version of the CVM: (2) would you pay $p as an entrance fee to this particular recreation site or prefer not to enter at this price? (amounts $p vary across respondents) Another is the multi-attribute DC version of CVM: (3) if you could choose between having the (policy-on) multiple-output bundle z 1 at price $p 1 or bundle z 0 (policy-off levels) at zero price, what would you choose? (different respondents are presented different bundles z 2, z 3,…, the same policy off bundle z 0, and different prices $p 2, $p 3,…).

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Still another is the Choice-experiment (CE) method: (4) if you could choose between having (z 1, $p 1 ), (z 2, $p 2 ), (z 3, $p 3 ) or (z 0, $0), what would you choose? (different respondents are presented different choice sets, which always include the policy-off option, i.e.: z 0 at price $0). Note that (3) is a particular case of (4), in which the choice set only includes two alternative options. Using formats (3) and (4) enhances the possibilities of transferring benefit information to other policy evaluation contexts, if the values of individual outputs are more transferable than the value of the overall multiple- output bundle.

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Behavioural, indirect methods Behavioural methods ask for actual behaviours or choices of the individuals in actual settings, e.g.: (5) how many visits did you pay to this particular recreation site last year? This is the travel cost (TC) method.

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interpreted as a demand curve and used to compute consumers surplus, as an estimate of the visitors WTP to gain access to the site. This is an indirect way to secure a WTP value that is directly elicited by OE CVM – the format (1) above. The TC method models the relationship between trip frequency and the implicit price of assess to the site – travel cost. This relationship is then:

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Deriving the value of each non-commodity output in one or two regression steps Variants of the travel cost method can be used to measure WTP for quality changes at the site as opposed to WTP to gain access to the site. Valuing quality changes is the most relevant application to measure WTP for landscape, water quality, biodiversity or other non-commodity outputs of land. For this purpose, we need to model, in a second regression step, the levels of these non-commodity outputs as shifters of an estimated demand relationship.

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WTP for a positive change in the level of a non- commodity output is then measured as the variation in consumers surplus resulting from that change. Note that WTP for the change in a non-commodity output is secured here only at a second regression step. Thus, with TC models, WTP for quality changes is two-steps away from observed data. Using OE CVM as in (1), WTP for the change in a non- commodity output is secured in a single regression step – by regressing WTP answers on site quality, across multiple sites.

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Estimating WTP values for quality changes in one single regression step is econometrically more efficient, i.e. gets more precise estimates of: the required marginal WTP (inverse demands) for the diverse non-commodity outputs of multifunctional land; and of demand interactions between these outputs. Formats (2) to (4) also enable us to estimate WTP for quality changes in a single regression step – by directly modelling individuals choices as determined by the levels of the multiple outputs and price.

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Benefits transfer In practical policy evaluations, analysts often make resort to previous valuation studies, and transfer valuation information from these studies to build the benefit estimates they need to evaluate a new policy. This practice is known as benefits transfer. This is todays dominant valuation practice, as it is cheaper than carrying out an original valuation study for each policy, and, especially, because carrying out such an original study is usually not compatible with the time constraints of the policy evaluation process.

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Benefits transfer and demand interactions There are many ways to carry out benefit transfers, some more reliable than others. There are many general problems with these transfer techniques. One of them is specific of multiple-output policy settings, and hence more relevant for the valuation of policies for multifunctional land. This problem has to do with the choice between carrying out: a disaggregate transfer or an aggregate transfer.

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A disaggregate transfer involves: separately looking for original studies in the literature for each of the policy outputs; separately transferring benefit estimates for each output, and then summing up across outputs. Advantage: makes the analyst list (hence recall) all of the policy outputs, which leads to completeness in benefit estimation. Limitation: prone to IVS bias.

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An aggregate transfer involves: looking for past valuation studies of complex multiple-output policies similar to the one we need a value for, and jointly transferring the original multiple-output benefit estimate as a whole. Advantage: not prone to IVS bias. Problems: usually impossible to find a past valuation study of a policy that is exactly the same as the one of interest with respect to all of the policy effects and the surveyed population; recall and cognitive problems for people in valuing multiple policy effects and the corresponding trade-offs in a single step.

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Thus, while simultaneous valuation of all of the multiple policy effects would be theoretically preferable, these recall and cognitive problems can make it practically impossible. This limitation has to do not with benefit transfers themselves but with original valuation studies. This is a very general limitation in the context of the empirical estimation of benefits of multiple-effect policies, such as policies affecting multifunctional land.

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Recommendations for policy evaluation In practice, the only possible way forward in many cases is keeping to the practicable IVS procedures, i.e.: disaggregate transfers or original benefit estimates prone to IVS bias. This stresses the need to seriously consider the suggestion, made earlier in this presentation, of extending the research on demand interactions to different policy contexts;

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in this way we can search for patterns of IVS biases common to similar contexts. These patterns will lead us to estimate the adjusting factor to be used in each context. These factors could then be systematically applied to correct for the large aggregation biases usually incurred in disaggregate transfers, as well as in IVS-based original benefit estimation, when these are the only practical alternatives.